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Found 4 papers, 3 papers with code
Scaling and evaluating sparse autoencoders
Using these techniques, we find clean scaling laws with respect to autoencoder size and sparsity.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control.
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research.
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